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Water is a major driver of food systems in arid and semi-arid countries. This paper explores the role of water in Egypt’s food system and the dilemma the country faces: raise food self-sufficiency by allocating freshwater resources from the Nile to food production, or rely on food imports from water-abundant regions worldwide. Using a food system analysis approach, the main drivers and outcomes in Egypt’s food system are described, followed by two examples of food system trade-offs where water plays an important role: wheat and chicken. The paper concludes with a reflection on the role of water in the food system and gives suggestions on how the role of water in the food system can be addressed systematically.

Het klimaat verandert. Het wordt natter en heter. Nederland moet zich aanpassen aan het veranderende klimaat. Daarom staan de gemeenten voor de uitdaging om er vanaf 2020 voor te zorgen dat een (her)inrichting van een straat of wijk klimaatbestendig is. Voor wateroverlast en waterveiligheid weten we ongeveer hoe dat moet. Voor hitte is eigenlijk nog niet duidelijk wat er moet gebeuren. En dat is zorgelijk, want hitte kan grote problemen geven. Hitte kan leiden tot extra sterfte en zal het leven in de stad bovendien steeds vaker onaangenaam maken. Duidelijk is dat er aandacht nodig is op het sociale vlak (o.a. in de zorg), voor gebouwen (koele binnenruimtes) en voor de buitenruimte. Dit rapport richt zich op dat laatste: op het hittebestendig inrichten van de buitenruimte, omdat daar nog veel kennis ontbreekt.

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.

Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate observed monthly runoff in 40 catchments, spatially distributed across 8 global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application ofa novel integrated evaluation metric to quantify the models’ ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and allindicators of upper and lower extreme runoff. There are particular challenges associated with reproducing both the timing and magnitude of seasonal cycles; the models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model – afinding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.

Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity to climate variability and climate change is comparable for impact models designed for either scale. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climate change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a better reproduction of reference conditions. However, the sensitivity of the two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases, but have distinct differences in other cases, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability. Whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models calibrated and validated against observed discharge should be used.